Audience measurement and feedback system

ABSTRACT

In one embodiment, a method includes receiving trick-mode start timestamps of trick-mode events performed on a video sequence in end-user devices such that each trick-mode start timestamp is associated with a trick-mode event performed on a video sequence in one end-user device, receiving trick-mode end timestamps of the trick-mode events, aggregating the trick-mode start timestamps according to a time value of each of the trick-mode start timestamps, aggregating the trick-mode end timestamps according to a time value of each of the trick-mode end timestamps, identifying a plurality of start clusters from the aggregation of the trick-mode start time stamps, identifying a plurality of end clusters from the aggregation of the trick-mode end time stamps, analyzing the plurality of start clusters and the plurality of end clusters, and identifying a level of engagement of a section of the video sequence based on the analyzing.

TECHNICAL FIELD

The present disclosure generally relates to audience measurement usingtrick-mode data.

BACKGROUND

Viewers use trick-modes to navigate video content including the fastforward and rewind trick-mode (also known as trick-play) functions tonavigate time shifted video assets, including cloud digital videorecorder (DVR) recordings, traditional (hard disk) DVR recordings, videoon demand (VOD) programs, and time-shifted live video. By way ofintroduction, a trick mode is a video playback mode characterized by aplayback mode other than the normal, forward, speed 1× (real timespeed). Therefore, a trick mode is characterized by a video viewingdirection and a speed, for example, faster or slower than 1× or steppingor pause or even 1× or any other speed in rewind. The objective of trickmodes is typically to review or resume viewing of video.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood and appreciated more fullyfrom the following detailed description, taken in conjunction with thedrawings in which:

FIG. 1 is a partly pictorial, partly block diagram view of an audiencemeasurement and feedback system and other elements constructed andoperative in accordance with an embodiment of the present disclosure;

FIG. 2 is a detailed block diagram view of the audience measurement andfeedback system of FIG. 1;

FIG. 3 is a flow chart showing an exemplary method of operation of thesystem of FIG. 1;

FIG. 4 is a diagram showing multiple trick-mode sub-events for use inthe system of FIG. 1;

FIG. 5 is a chart illustrating aggregation of trick-mode start and endtimestamps in time windows for use in the system of FIG. 1;

FIG. 6 is a chart showing clusters of trick-mode data for use in thesystem of FIG. 1; and

FIG. 7 is a chart showing more clusters of trick-mode data for use inthe system of FIG. 1.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

There is provided in accordance with an embodiment of the presentdisclosure, a method including receiving a plurality of trick-mode starttimestamps of a plurality of trick-mode events performed on a videosequence in a plurality of end-user devices such that each one of theplurality of trick-mode start timestamps is associated with a trick-modeevent of the plurality of trick-mode events performed on a videosequence in one of the plurality of end-user devices, receiving aplurality of trick-mode end timestamps of the plurality of trick-modeevents such that each one of the plurality of trick-mode end timestampsis associated with a trick-mode event of the plurality of trick-modeevents performed in one of the plurality of end-user devices,aggregating the plurality of trick-mode start timestamps a time value ofeach of the plurality of trick-mode start timestamps, aggregating theplurality of trick-mode end timestamps a time value of each of theplurality of trick-mode end timestamps, identifying a plurality of startclusters from the aggregation of the plurality of trick-mode start timestamps, identifying a plurality of end clusters from the aggregation ofthe plurality of trick-mode end time stamps, analyzing the plurality ofstart clusters and the plurality of end clusters, and identifying alevel of engagement of a section of the video sequence based on theanalyzing.

DETAILED DESCRIPTION

Reference is now made to FIG. 1, which is a partly pictorial, partlyblock diagram view of an audience measurement and feedback system 10 andother elements constructed and operative in accordance with anembodiment of the present disclosure. As subscribers 12 navigate throughvideo content using trick-mode fast forward and trick-mode rewind, thereceiver-decoder device 14 of each subscriber 12 tracks each trick-modeevent, including an associated trick-mode start time (corresponding towhen the subscriber 12 first pressed the fast-forward or rewind button)and an associated trick-mode end time (corresponding to when thesubscriber 12 released the fast-forward or rewind button or otherwiseresumed normal play) for each trick-mode event and an identification(ID) of the video content, for example, but not limited to: a contentID; or channel/service and date of the video content and optionally atitle of the video content. The trick-mode start times and end times aresaved as timestamps and are forwarded to the audience measurement andfeedback system 10 along with the ID of the content and optionally an IDof the receiver-decoder device 14 or another unique ID associated withthe receiver-decoder device 14 (for example, but not limited to, an IDof a smart card inserted into the receiver-decoder device 14) via asuitable transmission medium (for example, but not limited to, InternetProtocol, cable, cellular network) as trick-mode data 16 for analysis.The timestamps may be time offsets relative to the beginning of eachcontent item or timestamps included in the video sequence, for example,but not limited to, presentation time stamps (PTS) or program clockreference (PCR) of the video content. The timestamps may be derived fromvarious values, for example, but not limited to, press and release ofremote control keys or by the duration of time a key is pressed, or theconsecutive number of presses of the same key. However, the derivationof the entry point into a video sequence may need to reflect that thereis data trapped (or sitting) in the one or more repositories between theemission point (server, cloud etc.) and a video bit buffer employed by avideo decoder that reads a time stamp.

The trick-mode data 16 is processed by the audience measurement andfeedback system 10 to identify an engagement level of different sectionsof the content using the trick-mode data 16. The audience measurementand feedback system 10 assumes that content which has beenfast-forwarded over is low engagement content (e.g., objectionablecontent or boring content) and content which has been rewound to playagain is high engagement content (e.g., a sports highlight, a funnyscene), as will be described in more detail below with reference toFIGS. 3-7. The engagement levels are typically stored as engagement data18. The engagement data 18 may also include an identification of eachsection of the content having an engagement level and/or a copy of eachsection of the content having an engagement level. The engagement levelof a section of content may be defined as audience engagement in thesection of content and represents the degree that the viewers of thesection of content are actively viewing and interested in the content.For example, a highly engaged audience may discuss the content withother people in the room or online; a poorly engaged audience may checkunrelated messages on their phone or switch away from the contentaltogether. The engagement data 18 may be shared with thereceiver-decoder devices 14, one or more social media servers 20, one ormore content providers 22 and/or one or more content recommendationengines 24, by way of example only. The engagement data 18 may be sharedby sending the engagement data 18 to a remote device and/or by providingexternal access to the engagement data 18 residing on the audiencemeasurement and feedback system 10 via a user interface such as a webconsole.

Reference is now made to FIG. 2, which is a detailed block diagram viewof the audience measurement and feedback system 10 of FIG. 1. Theaudience measurement and feedback system 10 includes: a processor 26; amemory 28; a receiver 30; an output interface 32; a video editor 34including a decoder 36 and an encoder 38; a local bus 40; and a storagedevice 42. The memory 28 is operative to store data used by theprocessor 26. The local bus is operative to connect the elements of theaudience measurement and feedback system 10 together for data transferpurposes between the various elements of the audience measurement andfeedback system 10. The processor 26, the receiver 30, the outputinterface 32, the video editor 34 and the storage device 42 aredescribed in more detail below with reference to FIGS. 3-7.

Reference is now made to FIG. 3, which is a flow chart showing anexemplary method of operation of the system 10 of FIG. 1. The receiver30 (FIG. 2) is operative to receive the engagement data 18 (FIG. 1)including a plurality of trick-mode start timestamps and a plurality oftrick-mode end timestamps of a plurality of trick-mode events performedon a video sequence in a plurality of end-user devices 14 (block 44).

Reference is now made to FIG. 4, which is a diagram showing multipletrick-mode sub-events 48 for use in the system 10 of FIG. 1. Consecutivetrick-mode operations that occur in rapid succession may be combinedinto a single trick-mode event. The definition of “in rapid succession”is generally system configurable. By way of example only, trick-modeevents separated by less than 1-5 seconds may be good candidates formerging into a single trick-mode event. In the example of FIG. 4, thesubscriber 12 (FIG. 1) has first fast-forwarded (arrow 50) from positionP0 to position P1 in the video content, and then lets the content play(arrow 52) until position P2. The subscriber 12 then realizes thatposition P2 is too advanced in the content so the subscriber 12 thenrewinds (arrow 54) to position P3. The subscriber 12 then lets thecontent play (arrow 56) until position P4 and then realizes thatposition P4 is too early in the content. The subscriber 12 thenfast-forwards (arrow 58) to position P5. The three trick-mode eventsreally represent a single trick-mode event of fast-forwarding fromposition P0 to position P5.

Reference is again made to FIG. 3. The processor 26 (FIG. 2) isoperative to merge a first trick-mode event and a second trick-modeevent of the plurality of trick-mode events into a single trick-modeevent when the time elapsed between ending the first trick-mode eventand starting the second trick-mode event is less than a predeterminedvalue, for example, but not limited to, 1-5 seconds (block 60). It willbe appreciated that two or more trick-mode events may be merged togetherif the gap between adjacent trick-mode events is less than apredetermined value. It will be appreciated that merging trick-modesevents from the same subscriber 12 (FIG. 1) generally includesidentifying the trick-mode events which originate from the samesubscriber 12. The ID of the receiver-decoder device 14 or anotherunique ID associated with the receiver-decoder device 14 may be used toidentify trick-mode events originating from the same subscriber 12.

Reference is now made to FIG. 5, which is a chart illustratingaggregation of trick-mode start and end timestamps in time windows 62for use in the system 10 of FIG. 1. The received trick-mode start andend times are then aggregated according to a time value of thetrick-mode timestamps. A time value may be the value of a PTS or PCR orother timestamp measured in any suitable time unit, for example inseconds or milliseconds. FIG. 5 shows some start or end timestamps beingaggregated by time windows 62 yielding a histogram 66. The histogram 66of FIG. 5 includes one cluster 68 of timestamps representing part of atrick-mode event; either the start or end of a trick-mode event. Theheight of each bar 64 in the histogram 66 represents the number oftimestamps aggregated in each time window 62. The histogram 66 has agenerally bell shape distribution and will be described in more detailbelow with reference to FIG. 6. The histogram 66 has a mean value in thetime window of 9 seconds and the histogram has a magnitude. Themagnitude is the number of timestamps in the cluster 68 of the histogram66. In the example of FIG. 5, the cluster 68 has a magnitude of 1470representing 1470 trick-mode start or end timestamps. The cluster 68 mayalso have other statistical values such as standard deviation. Expandingthe time window axis enables plotting further clusters of timestamps,described below with reference to FIG. 6.

Reference is now made to FIG. 6, which is a chart 61 showing clusters 68of trick-mode data for use in the system 10 of FIG. 1. Reference is alsomade to FIG. 3. It should be noted that the trick-mode timestamps may beaggregated yielding the clusters 68 either by aggregating the timestampsinto time windows according to the time value of each timestamp or byaggregating the timestamps according to the time value of each timestampwithout aggregating the timestamps into time windows. FIG. 6 shows fourclusters 68 of trick mode data, clusters 68(1)-68(4). Clusters 68(1) and68(4) generally include start timestamps, based on the details includedin the received engagement data 18. The clusters 68(1) and 68(4) aredescribed herein as start clusters. Clusters 68(2) and 68(3) generallyinclude end timestamps (based on the details included in the receivedengagement data 18) and are described herein as end clusters. An endcluster may also be described as a high interest cluster as thesubscribers 12 are using a trick-mode to get to that point in thecontent and a start cluster may also be described as a low interestcluster as the subscribers 12 are using a trick-mode to leave that pointin the content. In the example of FIG. 6, the magnitude of start cluster68(1) is substantially the same as the magnitude of end cluster 68(2)even though start cluster 68(1) is shorter and wider than end cluster68(2). As both clusters 68(1), 68(2) have the same magnitude, it may beassumed that the same subscribers 12 (FIG. 1) started a trick-mode eventaround start cluster 68(1) and ended the trick-mode event around endcluster 68(2). Additionally, as the start cluster 68(1) precedes the endcluster 68(2) along the direction of a time line 72, it may be assumedthat the subscribers 12 are fast-forwarding (arrow 74) overnon-interesting content. It should be noted that playing content slowlyor stepping frame by frame and/or playing captions and/or raising volumemay be an indication that viewers are scrutinizing content and thereforein such a case the section may be of high interest and not low interest.Therefore, timestamps related to playing content slowly or steppingframe by frame and/or playing captions and/or raising volume may need tobe aggregated separately and analyzed accordingly. In the example ofFIG. 6, the magnitude of start cluster 68(4) is substantially the sameas the magnitude of end cluster 68(3). As both clusters 68(3), 68(4)have the same magnitude, it may be assumed that the same subscribers 12started a trick-mode event around start cluster 68(4) and ended thetrick-mode event around end cluster 68(3). Additionally, as the endcluster 68(3) precedes the start cluster 68(4) along the direction ofthe time line 72, it may be assumed that the subscribers 12 arerewinding (arrow 76) over interesting content to watch it again.

The processor 26 (FIG. 2) is operative to aggregate the trick-mode starttimestamps according to a time value of each trick-mode start timestampand aggregate the trick-mode end timestamps according to a time value ofeach trick-mode end timestamp (block 70). The term “aggregate” isdefined to include compiling the timestamps in accordance with a timevalue of the timestamps for use in statistical analysis. The processor26 is operative to: identify a plurality of start clusters 68 from theaggregation of the trick-mode start time stamps and identify a pluralityof end clusters 68 from the aggregation of the trick-mode end timestamps (block 78). Each of the start and end clusters 68 exhibits astatistical distribution having a plurality of statistical measurements,for example, but not limited to, mean and standard deviation. It shouldbe noted that the magnitude (the number of data points) of the cluster68 is an indication of engagement (the intensity of interest ordisinterest) in the section of content that the trick-mode event of thecluster 68 belongs to. The percentage of subscribers 12 represented inone of the clusters 68, also described herein as participation, may alsobe an indication of level engagement in a section of the content.

The processor 26 (FIG. 2) is operative to analyze the start clusters 68and the end clusters 68. The processor 26 (FIG. 2) is operative toidentify a level of engagement of a section of the video sequence basedon the analysis of the start clusters 68 and the end clusters 68 (block80). The sub-steps of block 80 is now described in more detail. Theprocessor 26 is operative to check if a measurement (e.g., magnitude) ofa start cluster 68 is within a predefined limit of a measurement (e.g.,magnitude) of an end cluster 68 (block 82) thereby providing a positiveindication that the start cluster and the end cluster 68 belong to thesame trick-mode event. The predefined limit may be any suitable limit,for example, the measurements of two clusters 68 may differ by a certainpercentage, for example, but not limited to, 5% or 20%. The processor 26will first start comparing the measurements of adjacent clusters 68before trying more distant clusters 68. If the measurement of the startcluster is within the predefined limit of the measurement of the endcluster 68 being compared (branch 84), the processor 26 is operative toidentify the level of engagement of the section of the video sequence(defined by the start and end cluster 68) depending on an order of theend cluster 68 and the start cluster 68 according to a time order of thevideo sequence (block 86). If the start cluster 68 precedes the endcluster 68 according to the time order of the video sequence, the levelof engagement is lower than if the end cluster 68 precedes the startcluster 68 according to the time order of the video sequence, asdescribed above. So the order of the start and end cluster 68 in thetime order gives a first approximation of the level of engagement of thesection of the content defined by the start cluster 68 and end cluster68.

If the measurements compared in the step of block 82 are not within thepredefined limit (branch 88), the processor 26 (FIG. 2) determines othercluster(s) 68 (starting with the most adjacent clusters 68 and thencontinuing with the next adjacent neighboring clusters 68) (block 90),if available (branch 94), for comparison in the step of block 82. Theprocessor 26 may select a new start cluster 68 and/or end cluster 68 forcomparison in the step of block 90. A limit may be set as to whichdistance of neighboring clusters 68 will be considered for comparisonpurposes. If there are no other clusters 68 (branch 96) for comparison,the process typically ends (block 92).

The processor 26 (FIG. 2) is operative to calculate the level ofengagement also using at least one statistical measurement of the endcluster 68 and/or the start cluster 68 (block 102). For example, iflevel of engagement is given a value between −10 and +10, +10 being themost interesting content, then if 5% of the subscribers 12 (FIG. 1) (5%participation) skip over a section of content that may indicate a levelof engagement of possibly −1, but if 95% of the subscribers 12 (95%participation) skip over the section of content that may indicate alevel of engagement of −10. It will be appreciated that the aboveexample can also be applied to high engagement content. By way ofanother example, a section of the content which has already been definedas high value or interesting or high engagement content based on theorder of the start and end clusters 68 defining the section, if themagnitude of the start or end cluster 68 is two standard deviations ormore above the mean value, then the section may be exceptionallyengaging and receive a score of +10. It will be appreciated that theratio of magnitude and standard deviation may be used to calculate ascore anywhere between 0 and +10. Similarly, it will be appreciated thatthe above example can also be applied to low engagement content tocalculate a score between 0 and −10.

The mean time offset values (lines 98) of the start and end clusters 68may be used to delineate the boundaries of the section of content. Sofor the section of content defined by the clusters 68(1), 68(2), themean time offsets 98(1), 98(2) may be used to define that section of thecontent. Similarly, for the section of content defined by the clusters68(3), 68(4), the mean time offsets 98(3), 98(4) may be used to definethat section of the content. Therefore, the processor 26 (FIG. 2) isoperative to determine the start and end of the section of the videosequence based on a statistical value (e.g., the mean) of the startcluster 68 and the end cluster 68, respectively (block 104).Alternatively, the boundaries of the section of content may be adjustedby another factor for example, but not limited plus or minus a fractionof a standard deviation of the cluster 68 concerned.

Reference is now made to FIG. 7, which is a chart 106 showing moreclusters 68 of trick-mode data for use in the system 10 of FIG. 1. Itshould be noted that in FIG. 7 two trick mode events have beenidentified shown by arrows 100. The arrows 100 show that the twotrick-mode events overlap. Although this is considered to be an unlikelyscenario, and may actually in some case indicate that both events shouldbe ignored due to contradictory data, the audience measurement andfeedback system 10 may still determine which clusters 68 match up witheach other to form trick mode events using the step of block 82 of FIG.3 where start and end clusters 68 are compared to determine if they havesimilar measurements, e.g., magnitudes.

Reference is again made to FIG. 3. The video editor 34 (FIG. 2) isoperative to decode the video sequence using the decoder 36 (FIG. 2) andre-encode the section of the video sequence using the encoder 38 (FIG.2) to encode the section of the video sequence as separately decodableitem. Alternatively, the start point of the section may be extended to aprevious random access point so that the section can be extracted fromthe original video sequence without requiring decoding and re-encoding.The processor 26 (FIG. 2) is operative to store, in the storage device42 (FIG. 2), the level of engagement of the section of the videosequence and the section of the video sequence and/or an identification(e.g., time offsets or timestamps) of the section of the video sequence(block 108). The statistical data about the clusters 68 may optionallybe stored in the storage device 42 for later use. The video segmentstorage policy may be configurable based on one or more of thefollowing: the source (e.g., who provided the content) of the content(e.g., how important the source of content is considered by an operatorof the audience measurement and feedback system 10 or a contentprovider, by way of example only); and the total number of segments tostore (e.g., an operator may only want to store a configurable number ofsegments and no more), by way of example only. Storing the sections ofvideo is generally performed to provide added context to the levels ofengagement calculated by the audience measurement and feedback system10.

The output interface 32 (FIG. 2) is operative to share the level ofengagement of the section of the video sequence and the section of thevideo sequence and/or an identification (e.g., time offsets ortimestamps) of the section of the video sequence with one or more of thefollowing: the receiver-decoder devices 14 (FIG. 1), one or more socialmedia servers 20 (FIG. 1), one or more content providers 22 (FIG. 1)and/or one or more content recommendation engines 24 (FIG. 1), by way ofexample only (block 110). The processor 26 (FIG. 2) checks if there aremore clusters 68 (FIG. 6) to analyze (block 112). If there are moreclusters 68 to analyze (branch 114), the processing continues with thestep of block 82. If there are no more clusters to analyze (branch 116),the process ends (block 118). It should be noted that the above stepsmay be performed in any suitable order. In particular, the sharing ofthe engagement data 18 (FIG. 1) may be performed per section of contentor in batch for several sections of content.

Reference is again made to FIG. 1. As the system 10 is fully automatedand does not require external metadata apart from the trick-mode data16, there are the following processing and bandwidth improvements:processing efficiencies are provided in the receiver-decoder devices 14(which do not need to process other audience measurements and run otheruser interfaces to receive user input of other audience measurements);bandwidth efficiencies are provided by minimizing the data transferredfrom the receiver-decoder devices 14 to the audience measurement andfeedback system 10; processing efficiencies are provided in the audiencemeasurement and feedback system 10 as aggregating the timestamp data andanalyzing the clusters 68 (FIG. 6) may be performed in batch for all thesubscribers 12 or a group of the subscribers 12.

The engagement data 18 may be used for social bookmarks marking contentwith a level of engagement and a group, for example, “group X likescontent section Y”. Content providers 22 may use the engagement data 18to decide which sections to skip and which sections to repeat and how toavoid boring plots in the future. The audience measurement and feedbacksystem 10 identifies both predictable points of interest (e.g. the startand end of a commercial break) and unpredictable events like a bigsports play highlight or a wardrobe malfunction. The audiencemeasurement and feedback system 10 allows content providers 22 to theinformatively decide how commercials and other content are presented tothe subscribers 12. The audience measurement and feedback system 10provides a new method for subscribers 12 to discover content based onthe engagement data 18.

In practice, some or all of these functions may be combined in a singlephysical component or, alternatively, implemented using multiplephysical components. These physical components may comprise hard-wiredor programmable devices, or a combination of the two. In someembodiments, at least some of the functions of the processing circuitrymay be carried out by a programmable processor under the control ofsuitable software. This software may be downloaded to a device inelectronic form, over a network, for example. Alternatively oradditionally, the software may be stored in tangible, non-transitorycomputer-readable storage media, such as optical, magnetic, orelectronic memory.

It is appreciated that software components may, if desired, beimplemented in ROM (read only memory) form. The software components may,generally, be implemented in hardware, if desired, using conventionaltechniques. It is further appreciated that the software components maybe instantiated, for example: as a computer program product or on atangible medium. In some cases, it may be possible to instantiate thesoftware components as a signal interpretable by an appropriatecomputer, although such an instantiation may be excluded in certainembodiments of the present disclosure.

It will be appreciated that various features of the disclosure whichare, for clarity, described in the contexts of separate embodiments mayalso be provided in combination in a single embodiment. Conversely,various features of the disclosure which are, for brevity, described inthe context of a single embodiment may also be provided separately or inany suitable sub-combination.

It will be appreciated by persons skilled in the art that the presentdisclosure is not limited by what has been particularly shown anddescribed hereinabove. Rather the scope of the disclosure is defined bythe appended claims and equivalents thereof.

What is claimed is:
 1. A method comprising: receiving a plurality oftrick-mode start timestamps of a plurality of trick-mode eventsperformed on a video sequence in a plurality of end-user devices suchthat each one of the plurality of trick-mode start timestamps isassociated with a trick-mode event of the plurality of trick-mode eventsperformed in one of the plurality of end-user devices; receiving aplurality of trick-mode end timestamps of the plurality of trick-modeevents such that each one of the plurality of trick-mode end timestampsis associated with a trick-mode event of the plurality of trick-modeevents performed in one of the plurality of end-user devices;aggregating the plurality of trick-mode start timestamps according to atime value of each of the plurality of trick-mode start timestamps;aggregating the plurality of trick-mode end timestamps according to atime value of each of the plurality of trick-mode end timestamps;identifying a plurality of start clusters from the aggregation of theplurality of trick-mode start time stamps; identifying a plurality ofend clusters from the aggregation of the plurality of trick-mode endtime stamps; analyzing the plurality of start clusters and the pluralityof end clusters; identifying a level of engagement of a section of thevideo sequence based on the analyzing; checking if a measurement of onestart cluster of the plurality of start clusters is within a predefinedlimit of a measurement of one end cluster of the plurality of endclusters: if the measurement of the one start cluster is within thepredefined limit of a measurement of the one end cluster, identifyingthe level of engagement of the section of the video sequence dependingon an order of the one end cluster and the one start cluster accordingto a time order of the video sequence; and providing processingefficiencies in an audience measurement and feedback system.
 2. Themethod according to claim 1, further comprising storing the level ofengagement of the section of the video sequence and at least one of: anidentification of the section of the video sequence; or the section ofthe video sequence.
 3. The method according to claim 1, furthercomprising identifying the level of engagement of the section of thevideo sequence depending on an order of one end cluster of the pluralityof end clusters and one start cluster of the plurality of start clustersaccording to a time order of the video sequence.
 4. The method accordingto claim 1, wherein if the one start cluster precedes the one endcluster according to the time order of the video sequence, the level ofengagement is lower than if the one end cluster precedes the one startcluster according to the time order of the video sequence.
 5. The methodaccording to claim 4, further comprising calculating the level ofengagement also using at least one statistical measurement of at leastone of: the one end cluster; or the one start cluster.
 6. The methodaccording to claim 1, further comprising determining a start and end ofthe section of the video sequence based on a statistical value of theone start cluster and the one end cluster, respectively.
 7. The methodaccording to claim 1, further comprising sharing the level of engagementof the section of the video sequence with one or more of the following:a social media source; an end-user device; a content provider; and acontent recommendation engine.
 8. The method according to claim 1,further comprising merging a first trick-mode event and a secondtrick-mode event of the plurality of trick-mode events into a singletrick-mode event when the time elapsed between ending the firsttrick-mode event and starting the second trick-mode event is less than apredetermined value.
 9. A system comprising: a receiver to: receive aplurality of trick-mode start timestamps of a plurality of trick-modeevents performed on a video sequence in a plurality of end-user devicessuch that each one of the plurality of trick-mode start timestamps isassociated with a trick-mode event of the plurality of trick-mode eventsperformed on a video sequence in one of the plurality of end-userdevices; and receive a plurality of trick-mode end timestamps of theplurality of trick-mode events such that each one of the plurality oftrick-mode end timestamps is associated with a trick-mode event of theplurality of trick-mode events performed in one of the plurality ofend-user devices; and a processor to: aggregate the plurality oftrick-mode start timestamps according to a time value of each of theplurality of trick-mode start timestamps; aggregate the plurality oftrick-mode end timestamps according to a time value of each of theplurality of trick-mode end timestamps; identify a plurality of startclusters from the aggregation of the plurality of trick-mode start timestamps; identify a plurality of end clusters from the aggregation of theplurality of trick-mode end time stamps; analyze the plurality of startclusters and the plurality of end clusters; identify a level ofengagement of a section of the video sequence from the analysis of theplurality of start clusters and the plurality of end clusters; check ifa measurement of one start cluster of the plurality of start clusters iswithin a predefined limit of a measurement of one end cluster of theplurality of end clusters: if the measurement of the one start clusteris within the predefined limit of a measurement of the one end cluster,identify the level of engagement of the section of the video sequencedepending on an order of the one end cluster and the one start clusteraccording to a time order of the video sequence; and provide processingefficiencies in the audience measurement and feedback system.
 10. Thesystem according to claim 9, wherein the processor is operative to storethe level of engagement of the section of the video sequence and atleast one of: the section of the video sequence; or an identification ofthe section of the video sequence.
 11. The system according to claim 9,wherein: each one of the plurality of start clusters and each one of theplurality of end clusters exhibits a statistical distribution having aplurality of statistical measurements; and the processor is operative tocalculate the level of engagement using at least one of the plurality ofstatistical measurements of at least one of: one end cluster of theplurality of end clusters; or one start cluster of the plurality ofstart clusters.
 12. The system according to claim 9, wherein theprocessor is operative to identify the level of engagement of thesection of the video sequence depending on an order of one end clusterof the plurality of end clusters and one start cluster of the pluralityof start clusters according to a time order of the video sequence. 13.The system according to claim 9, wherein if the one start clusterprecedes the one end cluster according to the time order of the videosequence, the level of engagement is lower than if the one end clusterprecedes the one start cluster according to the time order of the videosequence.
 14. The system according to claim 13 wherein the processor isoperative to calculate the level of engagement also using at least onestatistical measurement of at least one of: the one end cluster; or theone start cluster.
 15. The system according to claim 9, wherein theprocessor is operative to determine a start and end of the section ofthe video sequence based on a statistical value of the one start clusterand the one end cluster, respectively.
 16. The system according to claim9, further comprising an output interface to share the level ofengagement of the section of the video sequence with one or more of thefollowing: a social media server; an end-user device; a contentprovider; and a content recommendation engine.
 17. The system accordingto claim 9, wherein the processor is operative to merge a firsttrick-mode event and a second trick-mode event of the plurality oftrick-mode events into a single trick-mode event when the time elapsedbetween ending the first trick-mode event and starting the secondtrick-mode event is less than a predetermined value.